# Deep-seeded Clustering for Emotion Recognition from Wearable Physiological Sensors

arXiv: https://arxiv.org/abs/2308.09013 · HTML: https://mlsanity.com/paper/2308.09013/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 69 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [A comparison of LSTM and GRU networks for learning symbolic sequences](https://mlsanity.com/paper/2107.02248/) (2021)
- [A dataset of continuous affect annotations and physiological signals for emotion analysis](https://mlsanity.com/paper/1812.02782/) (2018)
- [Semi-supervised clustering methods](https://mlsanity.com/paper/1307.0252/) (2013)
- [Continuous Emotion Recognition during Music Listening Using EEG Signals: A Fuzzy Parallel Cascades Model](https://mlsanity.com/paper/1910.10489/) (2019)

## Abstract (arXiv)

According to the circumplex model of affect, an emotional response could characterized by a level of pleasure (valence) and intensity (arousal). As it reflects on the autonomic nervous system (ANS) activity, modern wearable wristbands can record non-invasively and during our everyday lives peripheral end-points of this response. While emotion recognition from physiological signals is usually achieved using supervised machine learning algorithms that require ground truth labels for training, collecting it is cumbersome and particularly unfeasible in naturalistic settings, and extracting meaningful insights from these signals requires domain knowledge and might be prone to bias. Here, we propose and test a deep-seeded clustering algorithm that automatically extracts and classifies features from those physiological signals with minimal supervision - combining an autoencoder (AE) for unsupervised feature representation and c-means clustering for fine-grained classification. We also show that the model obtains good performance results across three different datasets frequently used in affective computing studies (accuracies of 80.7% on WESAD, 64.2% on Stress-Predict and 61.0% on CEAP360-VR).